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# Multi-Agentic system Threat Modelling Guide
- URL: https://www.aigl.blog/multi-agentic-system-threat-modelling-guide/
- Published: 2025-04-28T13:01:41.000Z
- Updated: 2026-09-12T20:05:31.000Z
- Description: A highly structured guide showing how to apply OWASP’s Agentic AI Threat Taxonomy to real-world multi-agent systems (MAS), introducing the MAESTRO framework to surface layered vulnerabilities and new attack paths unique to agent-to-agent coordination environments.
- Author: Jakub Szarmach
- Tags: Guide, #aigl-library, Agentic AI, AI Security, Risk Management, Technical Controls, Model Evaluation

[Agentic-AI-MAS-Threat-Modelling-Guide-v1-FINALAgentic-AI-MAS-Threat-Modelling-Guide-v1-FINAL.pdf2 MBdownload-circle](https://www.aigl.blog/content/files/2025/04/Agentic-AI-MAS-Threat-Modelling-Guide-v1-FINAL.pdf "Download")

## **What’s Covered**

The **MAS Threat Modelling Guide** extends OWASP’s previous work on agentic threats by applying it to **multi-agent systems**—those involving numerous semi-autonomous agents working toward shared or distributed goals.

It’s built around the **MAESTRO Framework** (Multi-Agent Environment, Security, Threat, Risk, and Outcome), a layered architecture method that breaks down MAS security risks across foundation models, data operations, agent frameworks, deployment infrastructure, observability, compliance, and agent ecosystems.

Key highlights:

- **Expanded Threat Surface:** MAS coordination (agent-to-agent communication, decentralized task management) introduces unique risks like emergent behavior vulnerabilities, rogue agent proliferation, memory poisoning, agent collusion, and cascading trust failures.
- **Cross-Layer Threat Scenarios:** Detailed modeling shows how failures in one layer (e.g., foundation model bias or RAG poisoning) can propagate across the system.
- **Real-World Examples:** Deep-dive threat models were conducted for an RPA Expense Reimbursement Agent, Eliza OS (open-source agent OS for blockchain environments), and Anthropic’s Model Context Protocol (MCP).
- **Extended Threat Scenarios:** Beyond OWASP’s original taxonomy, the guide introduces \~20 new attack vectors specifically relevant to MAS, such as cross-agent feedback loop manipulation, misconfigured inter-agent monitoring, plugin vulnerabilities, and malicious bridge attacks in blockchain-connected agents.
- **Integration with MITRE ATT&CK & ATLAS:** Guidance is provided for connecting MAESTRO threat models to known adversarial tactics, enabling better defensive validation and telemetry alignment.

Overall, the guide transforms theoretical risks into concrete attack patterns, creating a blueprint for designing more resilient agentic AI architectures.

## **💡 Why it matters?**

MAS systems are moving from research into critical infrastructure, from warehouse robotics to cross-platform AI assistants.

Yet MAS introduce **scaling vulnerabilities** unlike anything seen in traditional software or standalone AI. A single compromised agent can cascade failure through an entire system—especially when agents are self-organizing or learning in live environments.

Threat modeling at the **multi-layer, cross-agent, and cross-system** level is no longer optional: it’s mandatory for resilience.

The MAS guide makes this possible by delivering **structured, repeatable threat modeling practices** using a layered methodology.

It also helps security architects surface **emergent vulnerabilities** that traditional AppSec methods would likely miss.

Without frameworks like MAESTRO, organizations risk building fragile MAS architectures blind to attack chains that unfold across autonomy, communication, and collective memory.

## **What’s Missing**

- **Risk Quantification Metrics:** While the guide is rich on threats and scenarios, it doesn’t provide scoring frameworks (e.g., impact, likelihood) to prioritize risks in MAS deployments.
- **Defensive Patterns Catalog:** Mitigations are implied but not detailed with recommended design patterns (e.g., agent isolation architectures, communication hardening techniques, sandboxing strategies).
- **Agent Lifecycle Focus:** More depth could have been given to threats that emerge during agent updates, retraining, or scaling (areas where real-world vulnerabilities often surface).
- **Broader Ecosystem Dependencies:** Integration risks with third-party plugins, cloud agent orchestration services, and cross-chain ecosystems are touched on but not deeply mapped.

Future iterations expanding these would make MAESTRO even more actionable for MAS system designers under operational pressure.

## **Best For**

- **AI system architects** designing multi-agent AI platforms.
- **Security engineers** responsible for LLM agentic deployments or autonomous system security.
- **Governance leads** shaping internal AI risk policies to align with emerging agent-centric frameworks.
- **Researchers and policymakers** who want concrete operational examples of agent risk beyond theory.

## **📚 Source Details**

**Title:** *Multi-Agentic System Threat Modelling Guide*

**Authors:** OWASP GenAI Security Project - Agentic Security Initiative Team (Ken Huang, Akram Sheriff, John Sotiropoulos et al.)

**Date:** April 22, 2025

**License:** Creative Commons (CC BY-SA 4.0)